An integrated deep neural network framework for predicting the wake flow in the wind field

Shanxun Sun, Shuangshuang Cui, Ting He and Qi Yao

Energy, 2024, vol. 291, issue C

Abstract: Ultra-short-term wake flow prediction is crucial for wind resource assessment and wind farm operation control. To improve the power generation efficiency and stable operation level of wind farms, a kind of prediction method is proposed that integrates the physical model and mathematical model into a deep neural network, enabling the prediction of the precise wake flow with sparse measured data. The proposed method can predict the entire flow field in real-time, providing accurate and reliable predictions for wind farm operation and management. The results of evaluation and validation of the integrated method show that the proposed method can accurately achieve ultra-short-term prediction, with a small error in all directions of velocity. Compared with the widely used LSTM neural network model and Multilayer Perceptron, there are certain advantages in both spatial and temporal scales, with a significant reduction in the average absolute error, indicating better generalization performance and prediction accuracy in the prediction of the wake flow field.

Keywords: Wake flow; Wind field; Ultra-short-term prediction; Integrated deep neural network (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:291:y:2024:i:c:s0360544224001713

DOI: 10.1016/j.energy.2024.130400

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